Skip to content

P
pytensor
提交 227be8bf authored 作者: Iban Harlouchet's avatar Iban Harlouchet
浏览文件
操作

Added multiple corrections to basic.py

上级 72798782
隐藏空白字符变更
内嵌 并排
正在显示 包含 22 行增加 和 84 行删除
theano/sparse/basic.py
浏览文件 @ 227be8bf
......@@ -578,7 +578,6 @@ class CSM(gof.Op):
_hashval = None
"""Pre-computed hash value, defined by __init__"""
__props__ = ()
def __init__(self, format, kmap=None):
if format not in ('csr', 'csc'):
......@@ -599,6 +598,13 @@ class CSM(gof.Op):
self._hashval = (hash(type(self)) ^ hash(self.format) ^
_kmap_hash(self.kmap))
def __eq__(self, other):
return (type(other) is CSM and other.format == self.format and
_kmap_eq(self.kmap, other.kmap))
def __hash__(self):
return self._hashval
def __str__(self):
if self.kmap is not None:
return "%s{%s}" % (self.__class__.__name__, str(self.kmap))
......@@ -752,7 +758,6 @@ class CSMGrad(gof.op.Op):
# 2. The elements in the sparse dimension are not guaranteed to be sorted.
# Therefore, the input data vector may have a different order than the
# gradient data vector.
__props__ = ("kmap",)
def __init__(self, kmap=None):
self.kmap = kmap
......@@ -761,6 +766,12 @@ class CSMGrad(gof.op.Op):
# if self.kmap is None:
# self.view_map = {0: [1]}
def __eq__(self, other):
return type(self) == type(other) and _kmap_eq(self.kmap, other.kmap)
def __hash__(self):
return 82345 ^ hash(type(self)) ^ _kmap_hash(self.kmap)
def __str__(self):
return "%s{%s}" % (
self.__class__.__name__,
......@@ -885,7 +896,7 @@ def cast(variable, dtype):
class DenseFromSparse(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = ()
__props__ = () # We don't put sparse_grad in the props.
def __init__(self, structured=True):
self.sparse_grad = structured
......@@ -1041,9 +1052,6 @@ class GetItemList(gof.op.Op):
return [GetItemListGrad(self)(x, indices, gout),
grad_undefined(self, 1, indices, "No gradient for this input")]
def __str__(self):
return self.__class__.__name__
get_item_list = GetItemList()
"""Select row of sparse matrix,
returning them as a new sparse matrix.
......@@ -1095,9 +1103,6 @@ class GetItemListGrad(gof.op.Op):
out[0] = y
def __str__(self):
return self.__class__.__name__
get_item_list_grad = GetItemListGrad()
......@@ -1132,9 +1137,6 @@ class GetItem2Lists(gof.op.Op):
grad_undefined(self, 1, ind1, "No gradient for this input"),
grad_undefined(self, 1, ind2, "No gradient for this input")]
def __str__(self):
return self.__class__.__name__
get_item_2lists = GetItem2Lists()
"""Select elements of sparse matrix, returning them in a vector.
......@@ -1185,9 +1187,6 @@ class GetItem2ListsGrad(gof.op.Op):
out[0] = y
def __str__(self):
return self.__class__.__name__
get_item_2lists_grad = GetItem2ListsGrad()
......@@ -1280,9 +1279,6 @@ class GetItem2d(gof.op.Op):
assert _is_sparse(x)
out[0] = x[start1:stop1:step1, start2:stop2:step2]
def __str__(self):
return self.__class__.__name__
get_item_2d = GetItem2d()
"""Implement a subtensor of sparse variable, returning a
sparse matrix.
......@@ -1350,9 +1346,6 @@ class GetItemScalar(gof.op.Op):
assert _is_sparse(x)
out[0] = theano._asarray(x[ind1, ind2], x.dtype)
def __str__(self):
return self.__class__.__name__
get_item_scalar = GetItemScalar()
"""Implement a subtensor of a sparse variable that takes
two scalars as index and returns a scalar.
......@@ -1499,9 +1492,6 @@ class ColScaleCSC(gof.op.Op):
def infer_shape(self, node, ins_shapes):
return [ins_shapes[0]]
def __str__(self):
return self.__class__.__name__
class RowScaleCSC(gof.op.Op):
# Scale each row of a sparse matrix by the corresponding element of
......@@ -1551,9 +1541,6 @@ class RowScaleCSC(gof.op.Op):
def infer_shape(self, node, ins_shapes):
return [ins_shapes[0]]
def __str__(self):
return self.__class__.__name__
def col_scale(x, s):
"""Scale each columns of a sparse matrix by the corresponding
......@@ -1598,7 +1585,12 @@ def row_scale(x, s):
class SpSum(gof.op.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = ("axis", "sparse_grad")
__props__ = ("axis",)
# WARNING: judgement call...
# We are not using the structured in the comparison or hashing
# because it doesn't change the perform method therefore, we
# *do* want Sums with different structured values to be merged
# by the merge optimization and this requires them to compare equal.
def __init__(self, axis=None, sparse_grad=True):
super(SpSum, self).__init__()
......@@ -1721,8 +1713,6 @@ class Diag(gof.op.Op):
def infer_shape(self, nodes, shapes):
return [(tensor.minimum(*shapes[0]), )]
def __str__(self):
return self.__class__.__name__
diag = Diag()
"""Extract the diagonal of a square sparse matrix as a dense vector.
......@@ -1769,8 +1759,6 @@ class SquareDiagonal(gof.op.Op):
def infer_shape(self, nodes, shapes):
return [(shapes[0][0], shapes[0][0])]
def __str__(self):
return self.__class__.__name__
square_diagonal = SquareDiagonal()
"""Return a square sparse (csc) matrix whose diagonal
is given by the dense vector argument.
......@@ -1856,9 +1844,6 @@ class AddSS(gof.op.Op):
# see the doc of add() for more detail.
__props__ = ()
def __str__(self):
return self.__class__.__name__
def make_node(self, x, y):
x, y = map(as_sparse_variable, [x, y])
assert x.format in ["csr", "csc"]
......@@ -1925,8 +1910,6 @@ class AddSSData(gof.op.Op):
def infer_shape(self, node, ins_shapes):
return [ins_shapes[0]]
def __str__(self):
return self.__class__.__name__
add_s_s_data = AddSSData()
"""Add two sparse matrices assuming they have the same sparsity
pattern.
......@@ -1948,9 +1931,6 @@ class AddSD(gof.op.Op):
# see the doc of add() for more detail.
__props__ = ()
def __str__(self):
return self.__class__.__name__
def make_node(self, x, y):
x, y = as_sparse_variable(x), tensor.as_tensor_variable(y)
assert x.format in ["csr", "csc"]
......@@ -2022,8 +2002,6 @@ class StructuredAddSV(gof.op.Op):
def infer_shape(self, node, ins_shapes):
return [ins_shapes[0]]
def __str__(self):
return self.__class__.__name__
structured_add_s_v = StructuredAddSV()
"""Structured addition of a sparse matrix and a dense vector.
The elements of the vector are only added to the corresponding
......@@ -2103,9 +2081,6 @@ class MulSS(gof.op.Op):
# See the doc of mul() for more detail
__props__ = ()
def __str__(self):
return self.__class__.__name__
def make_node(self, x, y):
x, y = as_sparse_variable(x), as_sparse_variable(y)
assert x.format in ["csr", "csc"]
......@@ -2142,9 +2117,6 @@ class MulSD(gof.op.Op):
# See the doc of mul() for more detail
__props__ = ()
def __str__(self):
return self.__class__.__name__
def make_node(self, x, y):
x, y = as_sparse_variable(x), tensor.as_tensor_variable(y)
......@@ -2276,8 +2248,6 @@ class MulSV(gof.op.Op):
def infer_shape(self, node, ins_shapes):
return [ins_shapes[0]]
def __str__(self):
return self.__class__.__name__
mul_s_v = MulSV()
"""Multiplication of sparse matrix by a broadcasted dense vector element wise.
......@@ -2370,9 +2340,6 @@ class __ComparisonOpSS(gof.op.Op):
def infer_shape(self, node, ins_shapes):
return [ins_shapes[0]]
def __str__(self):
return self.__class__.__name__
class __ComparisonOpSD(gof.op.Op):
"""
......@@ -2410,10 +2377,6 @@ class __ComparisonOpSD(gof.op.Op):
def infer_shape(self, node, ins_shapes):
return [ins_shapes[0]]
def __str__(self):
return self.__class__.__name__
def __ComparisonSwitch(SS, SD, DS):
"""
:param SS: function to apply between two sparses matrices.
......@@ -2769,17 +2732,13 @@ def vstack(blocks, format=None, dtype=None):
class Remove0(gof.Op):
# See doc in instance of this Op or a function after the class definition.
__props__ = ("inplace",)
def __init__(self, inplace=False):
self.inplace = inplace
if self.inplace:
self.destroy_map = {0: [0]}
def __eq__(self, other):
return type(self) == type(other) and self.inplace == other.inplace
def __hash__(self):
return 64153 ^ hash(type(self)) ^ hash(self.inplace)
def __str__(self):
l = []
if self.inplace:
......@@ -3056,9 +3015,6 @@ class TrueDot(gof.op.Op):
def __init__(self, grad_preserves_dense=True):
self.grad_preserves_dense = grad_preserves_dense
def __ne__(self, other):
return not (self == other)
def make_node(self, x, y):
# NOTE
# Because of trickiness of implementing,
......@@ -3133,10 +3089,6 @@ class TrueDot(gof.op.Op):
def infer_shape(self, node, shapes):
return [(shapes[0][0], shapes[1][1])]
def __str__(self):
return self.__class__.__name__
def true_dot(x, y, grad_preserves_dense=True):
"""
Operation for efficiently calculating the dot product when
......@@ -3182,9 +3134,6 @@ class StructuredDot(gof.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = ()
def __str__(self):
return self.__class__.__name__
def make_node(self, a, b):
a = as_sparse_variable(a)
......@@ -3318,9 +3267,6 @@ class StructuredDotGradCSC(gof.Op):
# matrix in csc format.
__props__ = ()
def __str__(self):
return self.__class__.__name__
def make_node(self, a_indices, a_indptr, b, g_ab):
return gof.Apply(self, [a_indices, a_indptr, b, g_ab],
[tensor.tensor(g_ab.dtype, (False,))])
......@@ -3451,9 +3397,6 @@ class StructuredDotGradCSR(gof.Op):
# matrix in csr format.
__props__ = ()
def __str__(self):
return self.__class__.__name__
def make_node(self, a_indices, a_indptr, b, g_ab):
return gof.Apply(self, [a_indices, a_indptr, b, g_ab],
[tensor.tensor(b.dtype, (False,))])
......@@ -3635,8 +3578,6 @@ class SamplingDot(gof.op.Op):
def infer_shape(self, node, ins_shapes):
return [ins_shapes[2]]
def __str__(self):
return self.__class__.__name__
sampling_dot = SamplingDot()
"""Operand for calculating the dot product dot(`x`, `y`.T) = `z` when you
only want to calculate a subset of `z`.
......@@ -3871,9 +3812,6 @@ class ConstructSparseFromList(gof.Op):
# See doc in instance of this Op or function after this class definition.
__props__ = ()
def __str__(self):
return self.__class__.__name__
def make_node(self, x, values, ilist):
"""
:param x: a dense matrix that specify the output shape.
......
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论